MIXOR: a computer program for mixed-effects ordinal regression analysis. MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

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  1. Mauff, Katya; Erler, Nicole S.; Kardys, Isabella; Rizopoulos, Dimitris: Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model (2021)
  2. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  3. Iannario, Maria: Hierarchical CUB models for ordinal variables (2012)
  4. Wu, Jianmin; Bentler, Peter M.: Application of H-likelihood to factor analysis models with binary response data (2012)
  5. Fox, Jean Paul: Bayesian item response modeling. Theory and applications. (2010)
  6. Meza, Cristian; Jaffr├ęzic, Florence; Foulley, Jean-Louis: Estimation in the probit normal model for binary outcomes using the SAEM algorithm (2009)
  7. Rabe-Hesketh, S.; Skrondal, A.; Gjessing, H. K.: Biometrical modeling of twin and family data using standard mixed model software (2008)
  8. Leon, Andrew C.; Hedeker, Donald: Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses (2007)
  9. Magidson, Jay; Vermunt, Jeroen K.: Use of latent class regression models with a random intercept to remove the effects of the overall response rating level (2006)
  10. Liu, Ivy; Agresti, Alan: The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion) (2005)
  11. Van Breukelen, Gerard J. P.: Psychometric modeling of response speed and accuracy with mixed and conditional regression (2005)
  12. Afshartous, David; De Leeuw, Jan: An application of multilevel model prediction to NELS:88 (2004)
  13. Rampichini, Carla; Grilli, Leonardo; Petrucci, Alessandra: Analysis of university course evaluations: from descriptive measures to multilevel models (2004)
  14. Skrondal, Anders; Rabe-Hesketh, Sophia: Generalized latent variable modeling. Multilevel, longitudinal, and structural equation models. (2004)
  15. Vermunt, Jeroen K.: An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models (2004)
  16. Fielding, Antony; Yang, Min; Goldstein, Harvey: Multilevel ordinal models for examination grades (2003)
  17. Moerbeek, Mirjam; Van Breukelen, Gerard J. P.; Berger, Martin: A comparison of estimation methods for multilevel logistic models (2003)
  18. Cam, Emmanuelle; Cadiou, Bernard; Hines, James E.; Monnat, Jean Yves: Influence of behavioural tactics on recruitment and reproductive trajectory in the Kittiwake (2002)
  19. Maples, Jerry J.; Murphy, Susan A.; Axinn, William G.: Two-level proportional hazards models (2002)
  20. Rausdenbush, Stephen W.; Bryk, Anthony S.: Hierarchical linear models. Applications and data analysis methods. (2002)

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